Modelling rainfall-runoff using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7615
- @Article{Whigham:2001:MCM,
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author = "P. A. Whigham and P. F. Crapper",
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title = "Modelling rainfall-runoff using genetic programming",
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journal = "Mathematical and Computer Modelling",
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volume = "33",
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pages = "707--721",
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year = "2001",
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number = "6-7",
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month = mar # "-" # apr,
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keywords = "genetic algorithms, genetic programming, Rainfall
runoff",
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ISSN = "0895-7177",
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DOI = "
doi:10.1016/S0895-7177(00)00274-0",
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URL = "
http://www.sciencedirect.com/science/article/B6V0V-42R1KRY-G/1/226d0ab4c2f13472b01ada47c8473fbf",
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abstract = "Genetic programming is an inductive form of machine
learning that evolves a computer program to perform a
task defined by a set of presented (training) examples
and has been successfully applied to problems that are
complex, nonlinear and where the size, shape, and
overall form of the solution are not explicitly known
in advance. We describe the application of a
grammatically-based genetic programming system to
discover rainfall-runoff relationships for two vastly
different catchments. A context-free grammar is used to
define the search space for the mathematical language
used to express the evolving programs. A daily time
series of rainfall-runoff is used to train the evolving
population. A deterministic lumped parameter model,
based on the unit hydrograph, is compared with the
results of the evolved models on an independent data
set. The favourable results of the genetic programming
approach show that machine learning techniques are
potentially a useful tool for developing hydrological
models, especially when surface water movement and
water losses are poorly understood.",
- }
Genetic Programming entries for
Peter Alexander Whigham
Peter F Crapper
Citations
